User manual PALISADE STATTOOLS 5.5

[. . . ] Guide to Using StatTools Statistics Add-In for Microsoft Excel ® Version 5. 5 May, 2009 Palisade Corporation 798 Cascadilla St Ithaca, NY 14850 (607) 277-8000 http://www. palisade. com Copyright Notice Copyright © 2009, Palisade Corporation. Trademark Acknowledgments Microsoft, Excel and Windows are registered trademarks of Microsoft, Inc. IBM is a registered trademark of International Business Machines, Inc. Palisade, TopRank, BestFit and RISKview are registered trademarks of Palisade Corporation. Welcome to StatTools for Excel Welcome StatTools gives Microsoft Excel - the industry-standard data analysis and modeling tool - a new, powerful statistics toolset!StatTools is a Microsoft Excel statistics add-in, allowing you to analyze data in Excel worksheets and work in the familiar Microsoft Office environment. [. . . ] So if the data change, the histogram and the test results change automatically. Reference: StatTools Menu Commands 104 Lilliefors Test Command Tests if observed data for a variable is normally distributed The Lilliefors Test procedure provides a more powerful test for normality than the more familiar chi-square goodness-of-fit test. (More powerful means that it is more likely to detect non-normality if it exists. ) It is based on a comparison of the "empirical cdf" and a normal cdf, where "cdf" stands for cumulative distribution function, showing the probability of being less than or equal to any particular value. For example, if there are 100 observations and the 13th smallest is 137, then the empirical cdf, evaluated at 137, is 0. 13. The Lilliefors test finds the maximum vertical distance between the empirical cdf and the normal cdf, and it compares this maximum to tabulated values (that are based on sample size). If the observed maximum vertical distance is sufficiently large, then we have evidence that the data do not come from a normal distribution. Lilliefors Test Dialog Box This analysis is set up using the Lilliefors Test dialog box: One or more variables can be selected for testing. The selected data set needs to be unstacked data. Variables can be from different data sets. Reference: StatTools Menu Commands 105 Lilliefors Test Report The results of the test are shown in the report above. There is no pvalue (as in most hypothesis tests), but we see from the statement that the maximum vertical distance is sufficiently large to cast doubt on the normality assumption. More evidence to this effect appears in the cdf's in the included chart. Actually, the fit between the two curves appears to be "pretty good, " and it might be good enough for all practical purposes. That is, we might conclude that these data are "close enough" to being normally distributed for our purposes. Reference: StatTools Menu Commands 106 Missing Data and Link to Data · · Missing Data - Missing data are allowed. All rows with missing data on the selected variables are ignored. Link to Data ­ The CDFs and all formulas for the test are linked to the original data. So if the data change, the graph and the test results change automatically. Reference: StatTools Menu Commands 107 Q-Q Normal Plot Command Tests if observed data for a variable is normally distributed The Q-Q Normal Plot command creates a quantile-quantile (Q-Q) plot for a single variable. It provides an informal test of normality. Although the details are somewhat complex, the objective is fairly simple: to compare the quantiles (or percentiles) for the data to the quantiles from a normal distribution. If the data are essentially normal, then the points on the Q-Q plot should be close to a 45-degree line. However, obvious curvature in the plot is an indication of some form of non-normality (skewness, for example). Q-Q Normal Plot Dialog Box This analysis is set up using the Q-Q Normal Plot dialog box: One variable can be selected for plotting. The selected data set needs to be unstacked data. This options in the Q-Q Normal Plot dialog box include: · Plot Using Standardized Q-Values - Specifies to use a standardized Q-Value, instead of Q-Q data, on the Y-axis of the graph. [. . . ] Data1, Data2, . . . DataN can be numbers, arrays, or ranges. Examples Guidelines STDDEVP Description STDDEVP (Data1, Data2, . . . DataN) calculates the population standard deviation of the data specified in Data1, Data2, . . . DataN. StatStdDevP(A1:A10, {1;2;3;2. 4}) calculates the population standard deviation of all value in the data set located in A1:A10 and the values 1, 2, 3 and 2. 4. Data1, Data2, . . . DataN can be numbers, arrays, or ranges. Examples Guidelines SUM Description Examples SUM(Data1, Data2, . . . DataN) calculates the sum of the data specified in Data1, Data2, . . . DataN. StatSum(A1:A10, {1;2;3;2. 4}) calculates the sum of all value in the data set located in A1:A10 and the values 1, 2, 3 and 2. 4. [. . . ]